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Evaluation of the post fire mechanical strength properties of recycled aggregate concrete containing GGBS: optimization and prediction using machine learning techniques

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Asian Journal of Civil Engineering 2023年第6期24卷 1639-1666页

作者： Tung, Tran Minh Babalola, Olusola Emmanuel Le, Duc-Hien Sustainable Developments in Civil Engineering Research Group Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam

This study investigates the residual compressive strength and modulus of elasticity of recycled aggregate concrete containing ground granulated blast furnace slag (RAC-GGBS) exposed to high temperatures, presents the numerical optimization of the mix design parameters of RAC-GGBS to achieve maximized residual compressive strength, and develops prediction models for residual compressive strength of RAC-GGBS based Gene expression programming (GEP) approach. The considered variables were the percentage replacement of RCA (50, 75, 100%), percentage replacement of GGBS (0, 20, 40, and 60%), water to binder ratio W/B (0.4 and 0.5), compressive strength (f_cu) and exposed temperatures of 25 ℃ (room temperature), 200, 400, 600, and 800 ℃. The experimental results show that addition of GGBS significantly enhanced the residual compressive strength and modulus of elasticity of RAC-GGBS while the SEM analysis conducted revealed a more compact microstructure with fewer pores and micro-cracks and better stabilized C–S–H hydrate formed at ITZ of RAC containing GGBS compared to RAC without GGBS after exposure to high temperature. The optimum condition obtained for RAC-GGBS exposed to high temperatures was 99.8%, 46.0% and 0.45 for RCA, GGBS and W/B, respectively, having maximized residual compressive strengths of 24.0, 25.5, 22.4, 17.2, 7.3 MPa after 25 ℃, 200 ℃, 400 ℃, 600 ℃ and 800 ℃ temperatures, respectively. The predictive ability of GEP model is high with R² values above 0.97 and low measured statistical errors. The mean absolute error (MAE) values of 0.889 and 1.244 and root mean square error (RMSE) of 1.064 and 1.502 for the training and validation datasets respectively, validate the GEP model's high accuracy and strong capacity to predict the residual compressive strength of RAC-GGBS. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Gene expression programming GGBS Recycled aggregate concrete residual compressive strength residual modulus of elasticity RSM optimization

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